The generation of detailed and accurate representations of a local environment, using dense simultaneous localization and mapping (dense SLAM) methods, for example, can be important for robotics applications such as navigation and scene interpretation. Although one obstacle to successfully producing such environmental representations has been the processing overhead required by dense SLAM, advances in computing technology have made that particular obstacle less formidable. For example, powerful graphics processing units (GPUs) enabling dense SLAM algorithms to harness the power of parallelization are now widely available.
Several further challenges need to be addressed in order to make dense SLAM suitable for real-world applications, however. For example, conventional dense SLAM systems typically do not scale to large-scale environments because they are constrained by GPU memory, thus limiting the size of the area that can be mapped. Another limitation is the inability to handle rapid or abrupt camera motion, which is particularly problematic for agile aerial robotic vehicles, such as aerial drones, for example.